code: def _ratio_scores(
      parameters_value,
      clusteringmodel_gmm_good,
      clusteringmodel_gmm_bad,
  ):
      """
      The ratio is smaller the better
      """
      ratio = clusteringmodel_gmm_good.score(
          [parameters_value]
      ) / clusteringmodel_gmm_bad.score([parameters_value])
      sigma = 0
      return ratio, sigma
  
file_path: nni/nni/algorithms/hpo/metis_tuner/Regression_GMM/Selection.py
imports: import os
  import random
  import sys
  from ..None import lib_acquisition_function
  from ..None import lib_constraint_summation
label_desc: Use image node_id 1 for calling a global function with example usage: _ratio_scores(parameters_value, clusteringmodel_gmm_good, clusteringmodel_gmm_bad) and returns: ratio,  sigma